---
title: "Bazzy: An AI Code Review Assistant With Context"
description: "Bazzy is the AI code review assistant giving context-aware suggestions across multi-repo, multi-language codebases, for faster reviews with fewer errors."
canonical: "https://baz.ai/resources/blog/meet-baazzy-the-ai-code-review-assistant"
---

# Bazzy: An AI Code Review Assistant With Context

Bazzy is the AI code review assistant giving context-aware suggestions across multi-repo, multi-language codebases, for faster reviews with fewer errors.

By Shacahar Azriel | 2025-06-26

In the code review process, developers find themselves on a goose hunt to understand the full impact of a given change. A change is identified in a given diff but to truly review the impacted files, you have to uncover multiple instances across files and locations to answer: W_here else is this element relevant?_ A simple question with a complicated journey to get there. Without this level of context, it’s even harder to receive quality [AI code generated suggestions as the industry has seen](https://baz.ai/resources/why-ai-code-assistants-fall-short).

This is exactly where Baz focused when building our first AI code review assistant: Meet Bazzy.

Bazzy is your AI code review assistant that **takes into account the conventions and across complex muli-repos, multi-language code bases** to help developers review changes faster. Ask anything about the change request from within the change request. Bazzy will suggest how to write better code and how to fix errors and issues detected. It’s our answer to move [beyond ‘looks good to me’ reviews](https://baz.ai/resources/beyond-lgtm-master-code-reviews-with-ai) to meaningful, intentional quality code. 

![Bazzy coding in action](/resources/meet-baazzy-the-ai-code-review-assistant-49.png)

Bazzy coding in action

**The experience that supports your code review workflow**

Powered by Anthropic’s Claude Sonnet 3.5, Bazzy provides the most accurate answers by leveraging the context of your change request across repos. Specifically, it analyzes the unique encapsulated elements of changes in a selected query so you receive recommendations grounded in the most relevance possible. 

When you’re using Bazzy in your code review process, you can:

-   Generate a code suggestion in one click to improve your code or adjust errors
-   Reduce context switching by leveraging an embedded chat interface
-   Access the most contextual suggestions supported by the unique context of your code with tracing and development data

‍

‍

**AI can accelerate your development with the right context** 

The intention of code reviews is to reduce incidents and produce better quality code, but when [current tools start slowing productivity down from AI generated noise](https://baz.ai/resources/copilot-vs-code-quality) - the teams suffer. Our goal is simple: to empower teams with tools that save time, enhance quality, and support continuous improvement in development.

Try out Baz in your code review process. [Get on the waitlist here](https://baz.ai/).

‍

‍